Mining of massive datasets / Jure Leskovec, Anand Rajaraman, Rocketship VC and Jeffrey David Ullman.

By: Leskovec, Jurij Contributor(s): Rajaraman, Anand | Ullman, Jeffrey DMaterial type: Text

TextLanguage: English Publication details: New york : Cambridge university press, 2020. Edition: 3rd edDescription: xi, 553 p. : ill. ; 24 cmISBN: 9781108476348Subject(s): Data mining | Big dataDDC classification: 006.312 Online resources: Worldcat details

Contents:

Table of contents Data mining MapReduce and the new software stack Finding similar items Mining data streams Link analysis Frequent itemsets Clustering Advertising on the Web Recommendation systems Mining social-network graphs Dimensionality reduction Large-scale machine learning Neural nets and deep learning

Summary: "The Web, social media, mobile activity, sensors, Internet commerce, and many other modern applications provide many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be used on even the largest datasets. It begins with a discussion of the MapReduce framework and related techniques for efficient parallel programming. The tricks of locality-sensitive hashing are explained. This body of knowledge, which deserves to be more widely known, is essential when seeking similar objects in a very large collection without having to compare each pair of objects. Stream-processing algorithms for mining data that arrives too fast for exhaustive processing are also explained. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering, each from the point of view that the data is too large to fit in main memory. Two applications: recommendation systems and Web advertising, each vital in e-commerce, are treated in detail. Later chapters cover algorithms for analyzing social-network graphs, compressing large-scale data, and machine learning. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs. Written by leading authorities in database and Web technologies, it is essential reading for students and practitioners alike"--

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode	Item holds
Text	Dr. S. R. Lasker Library, EWU Reserve Section	Non-fiction	006.312 23 LEM 2020 (Browse shelf(Opens below))	C-1	Not For Loan		31581
Text	Dr. S. R. Lasker Library, EWU Circulation Section	Non-fiction	006.312 23 LEM 2020 (Browse shelf(Opens below))	C-2	Available		31582

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Includes bibliographies, references and index.

Table of contents Data mining
MapReduce and the new software stack
Finding similar items
Mining data streams
Link analysis
Frequent itemsets
Clustering
Advertising on the Web
Recommendation systems
Mining social-network graphs
Dimensionality reduction
Large-scale machine learning
Neural nets and deep learning

"The Web, social media, mobile activity, sensors, Internet commerce, and many other modern applications provide many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be used on even the largest datasets. It begins with a discussion of the MapReduce framework and related techniques for efficient parallel programming. The tricks of locality-sensitive hashing are explained. This body of knowledge, which deserves to be more widely known, is essential when seeking similar objects in a very large collection without having to compare each pair of objects. Stream-processing algorithms for mining data that arrives too fast for exhaustive processing are also explained. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering, each from the point of view that the data is too large to fit in main memory. Two applications: recommendation systems and Web advertising, each vital in e-commerce, are treated in detail. Later chapters cover algorithms for analyzing social-network graphs, compressing large-scale data, and machine learning. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs. Written by leading authorities in database and Web technologies, it is essential reading for students and practitioners alike"--

Computer Science & Engineering Computer Science & Engineering

Jannatul Islam Muna

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